Overview

Brought to you by YData

Dataset statistics

Number of variables27
Number of observations464
Missing cells2211
Missing cells (%)17.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory98.0 KiB
Average record size in memory216.3 B

Variable types

Numeric20
Categorical3
DateTime1
Text3

Alerts

Avg.weight is highly overall correlated with Fruit and 4 other fieldsHigh correlation
Entry is highly overall correlated with Entry_species and 4 other fieldsHigh correlation
Entry_species is highly overall correlated with Entry and 1 other fieldsHigh correlation
Flowers_total is highly overall correlated with Marked and 1 other fieldsHigh correlation
Fruit is highly overall correlated with Avg.weight and 6 other fieldsHigh correlation
Fruits_marked is highly overall correlated with Avg.weight and 7 other fieldsHigh correlation
Fruits_notmarked is highly overall correlated with Fruits_totalHigh correlation
Fruits_total is highly overall correlated with Avg.weight and 7 other fieldsHigh correlation
Length_mm is highly overall correlated with Multiplied and 3 other fieldsHigh correlation
Marked is highly overall correlated with Flowers_total and 1 other fieldsHigh correlation
Multiplied is highly overall correlated with Length_mm and 3 other fieldsHigh correlation
Multiplied_cm is highly overall correlated with Length_mm and 3 other fieldsHigh correlation
Population is highly overall correlated with Entry and 2 other fieldsHigh correlation
Ratio_len/wid is highly overall correlated with Length_mm and 1 other fieldsHigh correlation
Species is highly overall correlated with Entry and 6 other fieldsHigh correlation
Tunnel_len is highly overall correlated with Multiplied and 2 other fieldsHigh correlation
Width_mm is highly overall correlated with Length_mm and 3 other fieldsHigh correlation
Wilted is highly overall correlated with Flowers_totalHigh correlation
seeds is highly overall correlated with Avg.weight and 4 other fieldsHigh correlation
weight is highly overall correlated with Avg.weight and 4 other fieldsHigh correlation
Tunnel_len has 31 (6.7%) missing values Missing
Entr_len has 31 (6.7%) missing values Missing
Entr_Height has 31 (6.7%) missing values Missing
Comments has 385 (83.0%) missing values Missing
Flowers_total has 268 (57.8%) missing values Missing
Marked has 268 (57.8%) missing values Missing
Wilted has 268 (57.8%) missing values Missing
Fruits_marked has 280 (60.3%) missing values Missing
Fruits_notmarked has 275 (59.3%) missing values Missing
Fruits_total has 284 (61.2%) missing values Missing
Fruit has 21 (4.5%) missing values Missing
seeds has 23 (5.0%) missing values Missing
weight has 23 (5.0%) missing values Missing
Avg.weight has 23 (5.0%) missing values Missing
Entry is uniformly distributed Uniform
Entry has unique values Unique
Wilted has 51 (11.0%) zeros Zeros
Fruits_marked has 65 (14.0%) zeros Zeros
Fruits_notmarked has 115 (24.8%) zeros Zeros
Fruits_total has 54 (11.6%) zeros Zeros
seeds has 235 (50.6%) zeros Zeros
weight has 235 (50.6%) zeros Zeros
Avg.weight has 235 (50.6%) zeros Zeros

Reproduction

Analysis started2025-05-17 08:32:08.681599
Analysis finished2025-05-17 08:33:28.584275
Duration1 minute and 19.9 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Entry
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct464
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean232.5
Minimum1
Maximum464
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-05-17T11:33:28.757799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile24.15
Q1116.75
median232.5
Q3348.25
95-th percentile440.85
Maximum464
Range463
Interquartile range (IQR)231.5

Descriptive statistics

Standard deviation134.08952
Coefficient of variation (CV)0.57672913
Kurtosis-1.2
Mean232.5
Median Absolute Deviation (MAD)116
Skewness0
Sum107880
Variance17980
MonotonicityStrictly increasing
2025-05-17T11:33:29.001724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
448 1
 
0.2%
447 1
 
0.2%
446 1
 
0.2%
445 1
 
0.2%
444 1
 
0.2%
443 1
 
0.2%
442 1
 
0.2%
441 1
 
0.2%
440 1
 
0.2%
439 1
 
0.2%
Other values (454) 454
97.8%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
464 1
0.2%
463 1
0.2%
462 1
0.2%
461 1
0.2%
460 1
0.2%
459 1
0.2%
458 1
0.2%
457 1
0.2%
456 1
0.2%
455 1
0.2%

Entry_species
Real number (ℝ)

High correlation 

Distinct295
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.94181
Minimum1
Maximum295
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-05-17T11:33:29.236864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8.15
Q139
median90.5
Q3179.25
95-th percentile271.85
Maximum295
Range294
Interquartile range (IQR)140.25

Descriptive statistics

Standard deviation85.385866
Coefficient of variation (CV)0.7627701
Kurtosis-0.88565433
Mean111.94181
Median Absolute Deviation (MAD)60.5
Skewness0.59674148
Sum51941
Variance7290.7461
MonotonicityNot monotonic
2025-05-17T11:33:29.517681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 3
 
0.6%
45 3
 
0.6%
22 3
 
0.6%
21 3
 
0.6%
28 3
 
0.6%
26 3
 
0.6%
25 3
 
0.6%
51 3
 
0.6%
18 3
 
0.6%
17 3
 
0.6%
Other values (285) 434
93.5%
ValueCountFrequency (%)
1 3
0.6%
2 3
0.6%
3 3
0.6%
4 3
0.6%
5 3
0.6%
6 3
0.6%
7 3
0.6%
8 3
0.6%
9 3
0.6%
10 3
0.6%
ValueCountFrequency (%)
295 1
0.2%
294 1
0.2%
293 1
0.2%
292 1
0.2%
291 1
0.2%
290 1
0.2%
289 1
0.2%
288 1
0.2%
287 1
0.2%
286 1
0.2%

Species
Categorical

High correlation 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
I.petrana
295 
I.atropurpurea
169 

Length

Max length14
Median length9
Mean length10.821121
Min length9

Characters and Unicode

Total characters5021
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowI.atropurpurea
2nd rowI.atropurpurea
3rd rowI.atropurpurea
4th rowI.atropurpurea
5th rowI.atropurpurea

Common Values

ValueCountFrequency (%)
I.petrana 295
63.6%
I.atropurpurea 169
36.4%

Length

2025-05-17T11:33:29.837447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-17T11:33:29.961607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
i.petrana 295
63.6%
i.atropurpurea 169
36.4%

Most occurring characters

ValueCountFrequency (%)
a 928
18.5%
r 802
16.0%
p 633
12.6%
I 464
9.2%
. 464
9.2%
e 464
9.2%
t 464
9.2%
u 338
 
6.7%
n 295
 
5.9%
o 169
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5021
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 928
18.5%
r 802
16.0%
p 633
12.6%
I 464
9.2%
. 464
9.2%
e 464
9.2%
t 464
9.2%
u 338
 
6.7%
n 295
 
5.9%
o 169
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5021
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 928
18.5%
r 802
16.0%
p 633
12.6%
I 464
9.2%
. 464
9.2%
e 464
9.2%
t 464
9.2%
u 338
 
6.7%
n 295
 
5.9%
o 169
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5021
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 928
18.5%
r 802
16.0%
p 633
12.6%
I 464
9.2%
. 464
9.2%
e 464
9.2%
t 464
9.2%
u 338
 
6.7%
n 295
 
5.9%
o 169
 
3.4%

Population
Categorical

High correlation 

Distinct3
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
YER
295 
KUR
117 
NET
52 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1392
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNET
2nd rowNET
3rd rowNET
4th rowNET
5th rowNET

Common Values

ValueCountFrequency (%)
YER 295
63.6%
KUR 117
 
25.2%
NET 52
 
11.2%

Length

2025-05-17T11:33:30.131462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-17T11:33:30.242851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
yer 295
63.6%
kur 117
 
25.2%
net 52
 
11.2%

Most occurring characters

ValueCountFrequency (%)
R 412
29.6%
E 347
24.9%
Y 295
21.2%
K 117
 
8.4%
U 117
 
8.4%
N 52
 
3.7%
T 52
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1392
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 412
29.6%
E 347
24.9%
Y 295
21.2%
K 117
 
8.4%
U 117
 
8.4%
N 52
 
3.7%
T 52
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1392
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 412
29.6%
E 347
24.9%
Y 295
21.2%
K 117
 
8.4%
U 117
 
8.4%
N 52
 
3.7%
T 52
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1392
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 412
29.6%
E 347
24.9%
Y 295
21.2%
K 117
 
8.4%
U 117
 
8.4%
N 52
 
3.7%
T 52
 
3.7%

Date
Date

Distinct15
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
Minimum2023-02-13 00:00:00
Maximum2023-03-28 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-17T11:33:30.401529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:30.591717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)

ID
Real number (ℝ)

Distinct163
Distinct (%)35.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean191.71121
Minimum1
Maximum921
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-05-17T11:33:30.841742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18.15
Q180
median161.5
Q3195
95-th percentile905
Maximum921
Range920
Interquartile range (IQR)115

Descriptive statistics

Standard deviation211.94311
Coefficient of variation (CV)1.1055332
Kurtosis6.8641019
Mean191.71121
Median Absolute Deviation (MAD)43.5
Skewness2.7677111
Sum88954
Variance44919.882
MonotonicityNot monotonic
2025-05-17T11:33:31.123242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
195 12
 
2.6%
159 9
 
1.9%
234 9
 
1.9%
193 9
 
1.9%
237 8
 
1.7%
179 7
 
1.5%
204 7
 
1.5%
165 7
 
1.5%
911 6
 
1.3%
47 6
 
1.3%
Other values (153) 384
82.8%
ValueCountFrequency (%)
1 1
 
0.2%
2 1
 
0.2%
3 1
 
0.2%
5 1
 
0.2%
7 2
 
0.4%
8 1
 
0.2%
9 3
0.6%
10 6
1.3%
13 1
 
0.2%
15 4
0.9%
ValueCountFrequency (%)
921 1
 
0.2%
920 1
 
0.2%
918 5
1.1%
917 1
 
0.2%
914 2
 
0.4%
911 6
1.3%
910 4
0.9%
908 2
 
0.4%
907 1
 
0.2%
905 3
0.6%

Flower_No.
Real number (ℝ)

Distinct9
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1939655
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-05-17T11:33:31.281424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4313243
Coefficient of variation (CV)0.65239142
Kurtosis2.6062748
Mean2.1939655
Median Absolute Deviation (MAD)1
Skewness1.5021666
Sum1018
Variance2.0486892
MonotonicityNot monotonic
2025-05-17T11:33:31.442345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 196
42.2%
2 119
25.6%
3 73
 
15.7%
4 41
 
8.8%
5 21
 
4.5%
6 7
 
1.5%
7 3
 
0.6%
8 3
 
0.6%
9 1
 
0.2%
ValueCountFrequency (%)
1 196
42.2%
2 119
25.6%
3 73
 
15.7%
4 41
 
8.8%
5 21
 
4.5%
6 7
 
1.5%
7 3
 
0.6%
8 3
 
0.6%
9 1
 
0.2%
ValueCountFrequency (%)
9 1
 
0.2%
8 3
 
0.6%
7 3
 
0.6%
6 7
 
1.5%
5 21
 
4.5%
4 41
 
8.8%
3 73
 
15.7%
2 119
25.6%
1 196
42.2%

Length_mm
Real number (ℝ)

High correlation 

Distinct272
Distinct (%)58.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.637694
Minimum26.7
Maximum98.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-05-17T11:33:31.651826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum26.7
5-th percentile42.23
Q151.15
median58
Q365.1
95-th percentile76.885
Maximum98.2
Range71.5
Interquartile range (IQR)13.95

Descriptive statistics

Standard deviation10.726084
Coefficient of variation (CV)0.18292131
Kurtosis0.44613549
Mean58.637694
Median Absolute Deviation (MAD)7.05
Skewness0.41579234
Sum27207.89
Variance115.04887
MonotonicityNot monotonic
2025-05-17T11:33:31.907574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55 7
 
1.5%
51.2 6
 
1.3%
54.2 5
 
1.1%
63.4 5
 
1.1%
61.2 5
 
1.1%
62 5
 
1.1%
65.5 4
 
0.9%
52.4 4
 
0.9%
51.4 4
 
0.9%
47.7 4
 
0.9%
Other values (262) 415
89.4%
ValueCountFrequency (%)
26.7 1
0.2%
30.9 1
0.2%
34.2 1
0.2%
38.2 1
0.2%
39 2
0.4%
39.1 1
0.2%
39.2 1
0.2%
39.7 1
0.2%
40 1
0.2%
40.1 1
0.2%
ValueCountFrequency (%)
98.2 1
0.2%
96 1
0.2%
92.7 1
0.2%
89.7 1
0.2%
89.1 1
0.2%
88.4 1
0.2%
86.5 1
0.2%
85.35 1
0.2%
85.1 1
0.2%
81.4 1
0.2%

Width_mm
Real number (ℝ)

High correlation 

Distinct232
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.904095
Minimum15.8
Maximum89.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-05-17T11:33:32.144170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15.8
5-th percentile45.345
Q152.7
median57.5
Q363.125
95-th percentile71.385
Maximum89.4
Range73.6
Interquartile range (IQR)10.425

Descriptive statistics

Standard deviation8.2810359
Coefficient of variation (CV)0.14301296
Kurtosis1.7156977
Mean57.904095
Median Absolute Deviation (MAD)5.1
Skewness0.093751332
Sum26867.5
Variance68.575556
MonotonicityNot monotonic
2025-05-17T11:33:32.425016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.3 8
 
1.7%
49.5 7
 
1.5%
53.4 6
 
1.3%
58.2 6
 
1.3%
55.2 6
 
1.3%
56.4 5
 
1.1%
57.4 5
 
1.1%
57.5 5
 
1.1%
52.6 5
 
1.1%
56 4
 
0.9%
Other values (222) 407
87.7%
ValueCountFrequency (%)
15.8 1
0.2%
37.6 1
0.2%
37.7 1
0.2%
40 1
0.2%
40.5 1
0.2%
40.8 1
0.2%
41.1 2
0.4%
41.4 1
0.2%
41.7 2
0.4%
41.9 1
0.2%
ValueCountFrequency (%)
89.4 1
0.2%
85.5 1
0.2%
81.8 1
0.2%
81.5 1
0.2%
79.1 1
0.2%
78.5 1
0.2%
77.8 1
0.2%
77.6 1
0.2%
77.3 2
0.4%
77 1
0.2%

Ratio_len/wid
Real number (ℝ)

High correlation 

Distinct460
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0182919
Minimum0.63859112
Maximum1.84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-05-17T11:33:32.712711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.63859112
5-th percentile0.75414723
Q10.92667027
median1.0228546
Q31.1201887
95-th percentile1.2355754
Maximum1.84
Range1.2014089
Interquartile range (IQR)0.19351841

Descriptive statistics

Standard deviation0.15356159
Coefficient of variation (CV)0.15080311
Kurtosis2.1442436
Mean1.0182919
Median Absolute Deviation (MAD)0.097373737
Skewness0.38048676
Sum472.48745
Variance0.023581163
MonotonicityNot monotonic
2025-05-17T11:33:32.991537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.079136691 2
 
0.4%
1.117001828 2
 
0.4%
0.9636363636 2
 
0.4%
1 2
 
0.4%
1.066433566 1
 
0.2%
1.046901173 1
 
0.2%
1.076779026 1
 
0.2%
1.135464231 1
 
0.2%
1.196 1
 
0.2%
1.207650273 1
 
0.2%
Other values (450) 450
97.0%
ValueCountFrequency (%)
0.6385911179 1
0.2%
0.6402349486 1
0.2%
0.6414473684 1
0.2%
0.6584615385 1
0.2%
0.6598101266 1
0.2%
0.6643159379 1
0.2%
0.6883802817 1
0.2%
0.6923076923 1
0.2%
0.7114285714 1
0.2%
0.7115135834 1
0.2%
ValueCountFrequency (%)
1.84 1
0.2%
1.689873418 1
0.2%
1.593617021 1
0.2%
1.395759717 1
0.2%
1.380846325 1
0.2%
1.331313131 1
0.2%
1.320723684 1
0.2%
1.312849162 1
0.2%
1.310580205 1
0.2%
1.305214724 1
0.2%

Multiplied
Real number (ℝ)

High correlation 

Distinct461
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3448.0896
Minimum421.86
Maximum8003.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-05-17T11:33:33.271713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum421.86
5-th percentile2017.305
Q12730.9375
median3364.32
Q33991.6325
95-th percentile5211.4575
Maximum8003.3
Range7581.44
Interquartile range (IQR)1260.695

Descriptive statistics

Standard deviation1012.4906
Coefficient of variation (CV)0.29363813
Kurtosis2.0911834
Mean3448.0896
Median Absolute Deviation (MAD)633.695
Skewness0.91078305
Sum1599913.6
Variance1025137.1
MonotonicityNot monotonic
2025-05-17T11:33:33.552633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2361.15 2
 
0.4%
3342.17 2
 
0.4%
3451.19 2
 
0.4%
3489.2 1
 
0.2%
3731.25 1
 
0.2%
3070.5 1
 
0.2%
4901.22 1
 
0.2%
6727.5 1
 
0.2%
6470.88 1
 
0.2%
5959.48 1
 
0.2%
Other values (451) 451
97.2%
ValueCountFrequency (%)
421.86 1
0.2%
1269.99 1
0.2%
1496.69 1
0.2%
1587.6 1
0.2%
1639.36 1
0.2%
1685.1 1
0.2%
1689.48 1
0.2%
1708 1
0.2%
1759.8 1
0.2%
1825.74 1
0.2%
ValueCountFrequency (%)
8003.3 1
0.2%
7965.54 1
0.2%
7536 1
0.2%
6727.5 1
0.2%
6609.51 1
0.2%
6470.88 1
0.2%
6444.25 1
0.2%
6282.24 1
0.2%
6040 1
0.2%
5959.48 1
0.2%

Multiplied_cm
Real number (ℝ)

High correlation 

Distinct461
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.480896
Minimum4.2186
Maximum80.033
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-05-17T11:33:33.814218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.2186
5-th percentile20.17305
Q127.309375
median33.6432
Q339.916325
95-th percentile52.114575
Maximum80.033
Range75.8144
Interquartile range (IQR)12.60695

Descriptive statistics

Standard deviation10.124906
Coefficient of variation (CV)0.29363813
Kurtosis2.0911834
Mean34.480896
Median Absolute Deviation (MAD)6.33695
Skewness0.91078305
Sum15999.136
Variance102.51371
MonotonicityNot monotonic
2025-05-17T11:33:34.052873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.6115 2
 
0.4%
33.4217 2
 
0.4%
34.5119 2
 
0.4%
34.892 1
 
0.2%
37.3125 1
 
0.2%
30.705 1
 
0.2%
49.0122 1
 
0.2%
67.275 1
 
0.2%
64.7088 1
 
0.2%
59.5948 1
 
0.2%
Other values (451) 451
97.2%
ValueCountFrequency (%)
4.2186 1
0.2%
12.6999 1
0.2%
14.9669 1
0.2%
15.876 1
0.2%
16.3936 1
0.2%
16.851 1
0.2%
16.8948 1
0.2%
17.08 1
0.2%
17.598 1
0.2%
18.2574 1
0.2%
ValueCountFrequency (%)
80.033 1
0.2%
79.6554 1
0.2%
75.36 1
0.2%
67.275 1
0.2%
66.0951 1
0.2%
64.7088 1
0.2%
64.4425 1
0.2%
62.8224 1
0.2%
60.4 1
0.2%
59.5948 1
0.2%

Tunnel_len
Real number (ℝ)

High correlation  Missing 

Distinct143
Distinct (%)33.0%
Missing31
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean27.561894
Minimum14.9
Maximum54.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-05-17T11:33:34.301636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum14.9
5-th percentile21.86
Q125.1
median27.5
Q330
95-th percentile33.7
Maximum54.9
Range40
Interquartile range (IQR)4.9

Descriptive statistics

Standard deviation3.7814549
Coefficient of variation (CV)0.13719866
Kurtosis5.6646738
Mean27.561894
Median Absolute Deviation (MAD)2.5
Skewness0.79983429
Sum11934.3
Variance14.299401
MonotonicityNot monotonic
2025-05-17T11:33:34.541844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29 9
 
1.9%
27.7 8
 
1.7%
30 8
 
1.7%
27.2 8
 
1.7%
26.4 8
 
1.7%
25.7 8
 
1.7%
30.3 7
 
1.5%
29.5 7
 
1.5%
28.9 7
 
1.5%
26.2 7
 
1.5%
Other values (133) 356
76.7%
(Missing) 31
 
6.7%
ValueCountFrequency (%)
14.9 1
0.2%
17 1
0.2%
19 1
0.2%
19.5 1
0.2%
19.6 1
0.2%
19.8 1
0.2%
20.4 1
0.2%
20.5 2
0.4%
20.8 2
0.4%
21 1
0.2%
ValueCountFrequency (%)
54.9 1
0.2%
37.1 1
0.2%
36.1 2
0.4%
35.5 1
0.2%
35.2 1
0.2%
35 2
0.4%
34.8 1
0.2%
34.7 1
0.2%
34.6 1
0.2%
34.5 1
0.2%

Entr_len
Real number (ℝ)

Missing 

Distinct95
Distinct (%)21.9%
Missing31
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean13.036259
Minimum4.9
Maximum19.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-05-17T11:33:34.810988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.9
5-th percentile9.66
Q111.5
median12.9
Q314.5
95-th percentile16.8
Maximum19.3
Range14.4
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2035749
Coefficient of variation (CV)0.16903431
Kurtosis0.10449603
Mean13.036259
Median Absolute Deviation (MAD)1.5
Skewness0.12187035
Sum5644.7
Variance4.8557425
MonotonicityNot monotonic
2025-05-17T11:33:35.071827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 13
 
2.8%
11.9 13
 
2.8%
12.7 11
 
2.4%
13 11
 
2.4%
11.8 10
 
2.2%
12.5 10
 
2.2%
14 10
 
2.2%
12.3 10
 
2.2%
11.3 9
 
1.9%
14.5 9
 
1.9%
Other values (85) 327
70.5%
(Missing) 31
 
6.7%
ValueCountFrequency (%)
4.9 1
 
0.2%
7.7 2
 
0.4%
8.2 1
 
0.2%
8.5 1
 
0.2%
8.8 2
 
0.4%
8.9 2
 
0.4%
9 1
 
0.2%
9.1 3
0.6%
9.2 1
 
0.2%
9.3 6
1.3%
ValueCountFrequency (%)
19.3 1
 
0.2%
19 1
 
0.2%
18.9 1
 
0.2%
18.8 1
 
0.2%
18.5 1
 
0.2%
18 4
0.9%
17.8 3
0.6%
17.7 1
 
0.2%
17.6 1
 
0.2%
17.5 2
0.4%

Entr_Height
Text

Missing 

Distinct92
Distinct (%)21.2%
Missing31
Missing (%)6.7%
Memory size3.8 KiB
2025-05-17T11:33:35.501545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length19
Median length3
Mean length3.0184758
Min length1

Characters and Unicode

Total characters1307
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)4.6%

Sample

1st row7.4
2nd row8.8
3rd row6.6
4th row7.2
5th row11.9
ValueCountFrequency (%)
9 17
 
3.9%
8.9 15
 
3.5%
8 14
 
3.2%
7.9 13
 
3.0%
9.9 12
 
2.8%
8.6 11
 
2.5%
9.3 11
 
2.5%
8.2 11
 
2.5%
8.5 11
 
2.5%
9.2 11
 
2.5%
Other values (83) 308
71.0%
2025-05-17T11:33:36.061814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 378
28.9%
1 166
12.7%
9 156
11.9%
8 132
 
10.1%
7 111
 
8.5%
6 88
 
6.7%
5 64
 
4.9%
3 62
 
4.7%
2 57
 
4.4%
4 48
 
3.7%
Other values (4) 45
 
3.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1307
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 378
28.9%
1 166
12.7%
9 156
11.9%
8 132
 
10.1%
7 111
 
8.5%
6 88
 
6.7%
5 64
 
4.9%
3 62
 
4.7%
2 57
 
4.4%
4 48
 
3.7%
Other values (4) 45
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1307
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 378
28.9%
1 166
12.7%
9 156
11.9%
8 132
 
10.1%
7 111
 
8.5%
6 88
 
6.7%
5 64
 
4.9%
3 62
 
4.7%
2 57
 
4.4%
4 48
 
3.7%
Other values (4) 45
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1307
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 378
28.9%
1 166
12.7%
9 156
11.9%
8 132
 
10.1%
7 111
 
8.5%
6 88
 
6.7%
5 64
 
4.9%
3 62
 
4.7%
2 57
 
4.4%
4 48
 
3.7%
Other values (4) 45
 
3.4%
Distinct440
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Memory size3.8 KiB
2025-05-17T11:33:36.516064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length26
Median length6
Mean length6.1314655
Min length2

Characters and Unicode

Total characters2845
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique432 ?
Unique (%)93.1%

Sample

1st row92803+92820
2nd row93612
3rd row93157
4th row123139
5th row134903
ValueCountFrequency (%)
22
 
4.7%
f 2
 
0.4%
cut 2
 
0.4%
143022 2
 
0.4%
104708 2
 
0.4%
113003 2
 
0.4%
123208 2
 
0.4%
eaten 2
 
0.4%
45022 1
 
0.2%
122011 1
 
0.2%
Other values (430) 430
91.9%
2025-05-17T11:33:37.136378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 765
26.9%
0 366
12.9%
4 328
11.5%
2 323
11.4%
3 293
 
10.3%
5 288
 
10.1%
9 105
 
3.7%
6 94
 
3.3%
7 87
 
3.1%
8 84
 
3.0%
Other values (11) 112
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2845
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 765
26.9%
0 366
12.9%
4 328
11.5%
2 323
11.4%
3 293
 
10.3%
5 288
 
10.1%
9 105
 
3.7%
6 94
 
3.3%
7 87
 
3.1%
8 84
 
3.0%
Other values (11) 112
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2845
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 765
26.9%
0 366
12.9%
4 328
11.5%
2 323
11.4%
3 293
 
10.3%
5 288
 
10.1%
9 105
 
3.7%
6 94
 
3.3%
7 87
 
3.1%
8 84
 
3.0%
Other values (11) 112
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2845
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 765
26.9%
0 366
12.9%
4 328
11.5%
2 323
11.4%
3 293
 
10.3%
5 288
 
10.1%
9 105
 
3.7%
6 94
 
3.3%
7 87
 
3.1%
8 84
 
3.0%
Other values (11) 112
 
3.9%

Comments
Text

Missing 

Distinct46
Distinct (%)58.2%
Missing385
Missing (%)83.0%
Memory size3.8 KiB
2025-05-17T11:33:37.451406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length31
Median length22
Mean length11.164557
Min length1

Characters and Unicode

Total characters882
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)40.5%

Sample

1st row1C, F eaten
2nd rowNumbers in pics
3rd row5C
4th rowC
5th row5C
ValueCountFrequency (%)
f 16
 
8.0%
cut 14
 
7.0%
c 10
 
5.0%
small 10
 
5.0%
seeds 10
 
5.0%
in 10
 
5.0%
fruit 10
 
5.0%
was 7
 
3.5%
size 6
 
3.0%
pages 6
 
3.0%
Other values (54) 100
50.3%
2025-05-17T11:33:37.981637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
122
 
13.8%
e 87
 
9.9%
s 65
 
7.4%
t 60
 
6.8%
i 41
 
4.6%
n 40
 
4.5%
a 37
 
4.2%
o 35
 
4.0%
r 34
 
3.9%
u 33
 
3.7%
Other values (40) 328
37.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 882
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
122
 
13.8%
e 87
 
9.9%
s 65
 
7.4%
t 60
 
6.8%
i 41
 
4.6%
n 40
 
4.5%
a 37
 
4.2%
o 35
 
4.0%
r 34
 
3.9%
u 33
 
3.7%
Other values (40) 328
37.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 882
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
122
 
13.8%
e 87
 
9.9%
s 65
 
7.4%
t 60
 
6.8%
i 41
 
4.6%
n 40
 
4.5%
a 37
 
4.2%
o 35
 
4.0%
r 34
 
3.9%
u 33
 
3.7%
Other values (40) 328
37.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 882
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
122
 
13.8%
e 87
 
9.9%
s 65
 
7.4%
t 60
 
6.8%
i 41
 
4.6%
n 40
 
4.5%
a 37
 
4.2%
o 35
 
4.0%
r 34
 
3.9%
u 33
 
3.7%
Other values (40) 328
37.2%

Flowers_total
Real number (ℝ)

High correlation  Missing 

Distinct16
Distinct (%)8.2%
Missing268
Missing (%)57.8%
Infinite0
Infinite (%)0.0%
Mean4.4132653
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-05-17T11:33:38.143767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile10
Maximum24
Range23
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.4593059
Coefficient of variation (CV)0.78384273
Kurtosis7.0929815
Mean4.4132653
Median Absolute Deviation (MAD)2
Skewness2.165714
Sum865
Variance11.966797
MonotonicityNot monotonic
2025-05-17T11:33:38.344578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2 36
 
7.8%
3 30
 
6.5%
1 30
 
6.5%
4 27
 
5.8%
5 18
 
3.9%
6 16
 
3.4%
7 12
 
2.6%
8 11
 
2.4%
9 5
 
1.1%
10 2
 
0.4%
Other values (6) 9
 
1.9%
(Missing) 268
57.8%
ValueCountFrequency (%)
1 30
6.5%
2 36
7.8%
3 30
6.5%
4 27
5.8%
5 18
3.9%
6 16
3.4%
7 12
 
2.6%
8 11
 
2.4%
9 5
 
1.1%
10 2
 
0.4%
ValueCountFrequency (%)
24 1
 
0.2%
18 1
 
0.2%
17 2
 
0.4%
14 2
 
0.4%
13 2
 
0.4%
12 1
 
0.2%
10 2
 
0.4%
9 5
1.1%
8 11
2.4%
7 12
2.6%

Marked
Real number (ℝ)

High correlation  Missing 

Distinct8
Distinct (%)4.1%
Missing268
Missing (%)57.8%
Infinite0
Infinite (%)0.0%
Mean2.3673469
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-05-17T11:33:39.891840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5583539
Coefficient of variation (CV)0.65827017
Kurtosis2.0958653
Mean2.3673469
Median Absolute Deviation (MAD)1
Skewness1.3586678
Sum464
Variance2.4284668
MonotonicityNot monotonic
2025-05-17T11:33:40.051382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 77
 
16.6%
2 46
 
9.9%
3 32
 
6.9%
4 20
 
4.3%
5 14
 
3.0%
6 4
 
0.9%
8 2
 
0.4%
9 1
 
0.2%
(Missing) 268
57.8%
ValueCountFrequency (%)
1 77
16.6%
2 46
9.9%
3 32
6.9%
4 20
 
4.3%
5 14
 
3.0%
6 4
 
0.9%
8 2
 
0.4%
9 1
 
0.2%
ValueCountFrequency (%)
9 1
 
0.2%
8 2
 
0.4%
6 4
 
0.9%
5 14
 
3.0%
4 20
 
4.3%
3 32
6.9%
2 46
9.9%
1 77
16.6%

Wilted
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct13
Distinct (%)6.6%
Missing268
Missing (%)57.8%
Infinite0
Infinite (%)0.0%
Mean2.0408163
Minimum0
Maximum16
Zeros51
Zeros (%)11.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-05-17T11:33:40.231376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile7
Maximum16
Range16
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.43023
Coefficient of variation (CV)1.1908127
Kurtosis8.4780397
Mean2.0408163
Median Absolute Deviation (MAD)1
Skewness2.4557032
Sum400
Variance5.9060178
MonotonicityNot monotonic
2025-05-17T11:33:40.441483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 55
 
11.9%
0 51
 
11.0%
2 31
 
6.7%
3 27
 
5.8%
4 11
 
2.4%
5 7
 
1.5%
6 3
 
0.6%
7 3
 
0.6%
9 3
 
0.6%
8 2
 
0.4%
Other values (3) 3
 
0.6%
(Missing) 268
57.8%
ValueCountFrequency (%)
0 51
11.0%
1 55
11.9%
2 31
6.7%
3 27
5.8%
4 11
 
2.4%
5 7
 
1.5%
6 3
 
0.6%
7 3
 
0.6%
8 2
 
0.4%
9 3
 
0.6%
ValueCountFrequency (%)
16 1
 
0.2%
13 1
 
0.2%
12 1
 
0.2%
9 3
 
0.6%
8 2
 
0.4%
7 3
 
0.6%
6 3
 
0.6%
5 7
 
1.5%
4 11
2.4%
3 27
5.8%

Fruits_marked
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct6
Distinct (%)3.3%
Missing280
Missing (%)60.3%
Infinite0
Infinite (%)0.0%
Mean1.2065217
Minimum0
Maximum5
Zeros65
Zeros (%)14.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-05-17T11:33:40.613363image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3.85
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2284251
Coefficient of variation (CV)1.0181542
Kurtosis0.40840635
Mean1.2065217
Median Absolute Deviation (MAD)1
Skewness0.97538336
Sum222
Variance1.5090283
MonotonicityNot monotonic
2025-05-17T11:33:40.751434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 65
 
14.0%
1 58
 
12.5%
2 32
 
6.9%
3 19
 
4.1%
4 7
 
1.5%
5 3
 
0.6%
(Missing) 280
60.3%
ValueCountFrequency (%)
0 65
14.0%
1 58
12.5%
2 32
6.9%
3 19
 
4.1%
4 7
 
1.5%
5 3
 
0.6%
ValueCountFrequency (%)
5 3
 
0.6%
4 7
 
1.5%
3 19
 
4.1%
2 32
6.9%
1 58
12.5%
0 65
14.0%

Fruits_notmarked
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct7
Distinct (%)3.7%
Missing275
Missing (%)59.3%
Infinite0
Infinite (%)0.0%
Mean0.71428571
Minimum0
Maximum9
Zeros115
Zeros (%)24.8%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-05-17T11:33:40.911774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2040964
Coefficient of variation (CV)1.6857349
Kurtosis12.564255
Mean0.71428571
Median Absolute Deviation (MAD)0
Skewness2.8209374
Sum135
Variance1.449848
MonotonicityNot monotonic
2025-05-17T11:33:41.061665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 115
24.8%
1 40
 
8.6%
2 19
 
4.1%
3 10
 
2.2%
4 2
 
0.4%
5 2
 
0.4%
9 1
 
0.2%
(Missing) 275
59.3%
ValueCountFrequency (%)
0 115
24.8%
1 40
 
8.6%
2 19
 
4.1%
3 10
 
2.2%
4 2
 
0.4%
5 2
 
0.4%
9 1
 
0.2%
ValueCountFrequency (%)
9 1
 
0.2%
5 2
 
0.4%
4 2
 
0.4%
3 10
 
2.2%
2 19
 
4.1%
1 40
 
8.6%
0 115
24.8%

Fruits_total
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct10
Distinct (%)5.6%
Missing284
Missing (%)61.2%
Infinite0
Infinite (%)0.0%
Mean1.9666667
Minimum0
Maximum13
Zeros54
Zeros (%)11.6%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-05-17T11:33:41.221777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile6
Maximum13
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.062975
Coefficient of variation (CV)1.0489703
Kurtosis4.0869682
Mean1.9666667
Median Absolute Deviation (MAD)1
Skewness1.5547052
Sum354
Variance4.2558659
MonotonicityNot monotonic
2025-05-17T11:33:41.421846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 54
 
11.6%
1 38
 
8.2%
3 29
 
6.2%
2 26
 
5.6%
4 13
 
2.8%
5 8
 
1.7%
6 7
 
1.5%
8 2
 
0.4%
7 2
 
0.4%
13 1
 
0.2%
(Missing) 284
61.2%
ValueCountFrequency (%)
0 54
11.6%
1 38
8.2%
2 26
5.6%
3 29
6.2%
4 13
 
2.8%
5 8
 
1.7%
6 7
 
1.5%
7 2
 
0.4%
8 2
 
0.4%
13 1
 
0.2%
ValueCountFrequency (%)
13 1
 
0.2%
8 2
 
0.4%
7 2
 
0.4%
6 7
 
1.5%
5 8
 
1.7%
4 13
 
2.8%
3 29
6.2%
2 26
5.6%
1 38
8.2%
0 54
11.6%

Fruit
Categorical

High correlation  Missing 

Distinct2
Distinct (%)0.5%
Missing21
Missing (%)4.5%
Memory size3.8 KiB
1.0
222 
0.0
221 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1329
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 222
47.8%
0.0 221
47.6%
(Missing) 21
 
4.5%

Length

2025-05-17T11:33:41.602549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-17T11:33:41.705590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 222
50.1%
0.0 221
49.9%

Most occurring characters

ValueCountFrequency (%)
0 664
50.0%
. 443
33.3%
1 222
 
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1329
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 664
50.0%
. 443
33.3%
1 222
 
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1329
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 664
50.0%
. 443
33.3%
1 222
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1329
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 664
50.0%
. 443
33.3%
1 222
 
16.7%

seeds
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct58
Distinct (%)13.2%
Missing23
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean11.222222
Minimum0
Maximum79
Zeros235
Zeros (%)50.6%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-05-17T11:33:41.881554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q319
95-th percentile44
Maximum79
Range79
Interquartile range (IQR)19

Descriptive statistics

Standard deviation16.202148
Coefficient of variation (CV)1.4437558
Kurtosis1.982125
Mean11.222222
Median Absolute Deviation (MAD)0
Skewness1.5464603
Sum4949
Variance262.5096
MonotonicityNot monotonic
2025-05-17T11:33:42.151437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 235
50.6%
6 10
 
2.2%
22 9
 
1.9%
16 9
 
1.9%
5 8
 
1.7%
11 8
 
1.7%
7 7
 
1.5%
28 7
 
1.5%
33 6
 
1.3%
27 6
 
1.3%
Other values (48) 136
29.3%
(Missing) 23
 
5.0%
ValueCountFrequency (%)
0 235
50.6%
2 2
 
0.4%
3 4
 
0.9%
4 3
 
0.6%
5 8
 
1.7%
6 10
 
2.2%
7 7
 
1.5%
8 4
 
0.9%
9 1
 
0.2%
10 5
 
1.1%
ValueCountFrequency (%)
79 2
0.4%
76 1
0.2%
64 1
0.2%
63 1
0.2%
62 1
0.2%
60 1
0.2%
59 1
0.2%
57 1
0.2%
53 2
0.4%
51 1
0.2%

weight
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct205
Distinct (%)46.5%
Missing23
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean360.29887
Minimum0
Maximum3166.2
Zeros235
Zeros (%)50.6%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-05-17T11:33:42.411557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3568.8
95-th percentile1482.4
Maximum3166.2
Range3166.2
Interquartile range (IQR)568.8

Descriptive statistics

Standard deviation550.36669
Coefficient of variation (CV)1.5275282
Kurtosis3.3233108
Mean360.29887
Median Absolute Deviation (MAD)0
Skewness1.8203468
Sum158891.8
Variance302903.5
MonotonicityNot monotonic
2025-05-17T11:33:42.645724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 235
50.6%
1085.2 2
 
0.4%
305.6 2
 
0.4%
1102.1 1
 
0.2%
758.9 1
 
0.2%
272.3 1
 
0.2%
104.1 1
 
0.2%
220.5 1
 
0.2%
2128.5 1
 
0.2%
280.2 1
 
0.2%
Other values (195) 195
42.0%
(Missing) 23
 
5.0%
ValueCountFrequency (%)
0 235
50.6%
16.1 1
 
0.2%
64.8 1
 
0.2%
68.9 1
 
0.2%
77.4 1
 
0.2%
85.3 1
 
0.2%
103.9 1
 
0.2%
104.1 1
 
0.2%
105.3 1
 
0.2%
108.3 1
 
0.2%
ValueCountFrequency (%)
3166.2 1
0.2%
2565.6 1
0.2%
2509.9 1
0.2%
2359.8 1
0.2%
2286.5 1
0.2%
2205.4 1
0.2%
2128.5 1
0.2%
2084.4 1
0.2%
2060.3 1
0.2%
1977.8 1
0.2%

Avg.weight
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct206
Distinct (%)46.7%
Missing23
Missing (%)5.0%
Infinite0
Infinite (%)0.0%
Mean15.117491
Minimum0
Maximum76.024
Zeros235
Zeros (%)50.6%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2025-05-17T11:33:42.883974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q329.968182
95-th percentile46.259091
Maximum76.024
Range76.024
Interquartile range (IQR)29.968182

Descriptive statistics

Standard deviation18.134739
Coefficient of variation (CV)1.1995866
Kurtosis-0.31573945
Mean15.117491
Median Absolute Deviation (MAD)0
Skewness0.81414818
Sum6666.8134
Variance328.86876
MonotonicityNot monotonic
2025-05-17T11:33:43.141797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 235
50.6%
27.82564103 2
 
0.4%
48.90606061 1
 
0.2%
25.63023256 1
 
0.2%
45.38333333 1
 
0.2%
72.03571429 1
 
0.2%
34.7 1
 
0.2%
44.1 1
 
0.2%
47.3 1
 
0.2%
40.02857143 1
 
0.2%
Other values (196) 196
42.2%
(Missing) 23
 
5.0%
ValueCountFrequency (%)
0 235
50.6%
8.05 1
 
0.2%
8.554545455 1
 
0.2%
9.775 1
 
0.2%
9.842857143 1
 
0.2%
11.67272727 1
 
0.2%
13.26363636 1
 
0.2%
13.50833333 1
 
0.2%
14.54615385 1
 
0.2%
15.48 1
 
0.2%
ValueCountFrequency (%)
76.024 1
0.2%
74.03043478 1
0.2%
72.03571429 1
0.2%
69.44444444 1
0.2%
62.68181818 1
0.2%
62.43333333 1
0.2%
62.24074074 1
0.2%
61.30588235 1
0.2%
58.76818182 1
0.2%
58.37692308 1
0.2%

Interactions

2025-05-17T11:33:23.421651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:10.657741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:14.041788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:17.371799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:23.633759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:27.284611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:31.373688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:34.889820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:39.021853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:42.731688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:46.222655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:50.651590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:54.153326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:57.374315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:01.834440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:05.267480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:08.563822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:11.751641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:15.062276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:19.812359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:23.611596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:10.793479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:14.220663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:17.524460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:23.821612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:27.453783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:31.536099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:35.049669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:39.181472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:42.891751image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:46.406006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:50.831806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:54.321773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:57.531743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:01.982321image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:05.422746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:08.713770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:11.901546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:15.243249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:19.981723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:23.801451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:10.941651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:14.397408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:17.693521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:24.017618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:28.079314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:31.721969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:35.240513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:39.401557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:43.061655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:46.604110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:51.001840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:54.481661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:57.699065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:02.181806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:05.581564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:08.862597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:12.071630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:15.441783image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:20.131676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:23.976549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:11.093213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:14.582495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:17.847615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:24.211831image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:28.257549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:31.871701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:35.393368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:39.591603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:43.221861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:46.786545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:51.179198image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:54.624462image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:57.863919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:02.341628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:05.729861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-17T11:33:10.593889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:13.847635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:18.531622image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:22.230854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:26.081449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:13.073103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:16.271403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:22.639511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:26.237432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:30.267671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:33.823571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:37.993605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:41.687405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:45.221474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:49.650580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:53.151544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:56.385078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:59.881616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:04.199004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:07.546429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:10.751594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:14.011641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:18.711529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:22.396980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:26.261405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:13.226617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:16.444302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:22.801666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:26.391855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:30.446254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:33.951391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:38.163043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:41.838053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:45.391803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:49.801440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:53.301607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:56.556595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:00.075718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:04.371431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:07.711538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:10.913771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:14.181562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:18.891436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:22.551387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:26.435941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:13.371441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:16.611773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:22.931428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:26.544290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:30.614107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:34.101517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:38.327362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:42.005979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:45.541485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:49.951482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:53.453561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:56.713325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:00.222493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:04.531467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:07.864658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:11.151651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:14.355126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:19.082455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:22.721904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:26.591519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:13.542582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:16.781621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:23.101828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:26.709244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:30.773855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:34.258149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:38.491690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:42.161799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:45.701424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:50.118965image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:53.582453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:56.883915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:00.406853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:04.731558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:08.031454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:11.304515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:14.511556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:19.251579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:22.897425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:26.797699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:13.716280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:16.993704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:23.271593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:26.909902image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:30.981786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:34.450747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:38.681711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:42.365634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:45.883047image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:50.298028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:53.781426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:57.051620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:01.481803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:04.904769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:08.201725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:11.451608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:14.701432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:19.431413image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:23.061645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:26.971712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:13.883912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:17.181558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:23.452459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:27.091651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:31.171566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:34.707757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:38.847569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:42.550848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:46.063201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:50.482793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:53.951385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:32:57.221468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:01.661958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:05.086929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:08.371664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:11.601589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:14.893648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:19.621504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-17T11:33:23.221465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-05-17T11:33:43.383204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Avg.weightEntr_lenEntryEntry_speciesFlower_No.Flowers_totalFruitFruits_markedFruits_notmarkedFruits_totalIDLength_mmMarkedMultipliedMultiplied_cmPopulationRatio_len/widSpeciesTunnel_lenWidth_mmWiltedseedsweight
Avg.weight1.0000.2630.3160.225-0.1000.1090.9280.6840.3210.628-0.1660.0320.1470.0980.0980.346-0.1510.4940.3890.1780.0050.8740.925
Entr_len0.2631.000-0.0260.037-0.0940.0590.2650.1870.1280.188-0.1250.2610.0610.3180.3180.302-0.0010.1590.4700.3440.0240.2660.278
Entry0.316-0.0261.0000.5490.0710.0050.4740.5290.3840.5370.293-0.4350.140-0.340-0.3400.926-0.4600.939-0.001-0.141-0.0930.3660.348
Entry_species0.2250.0370.5491.0000.204-0.1950.2910.1570.1730.191-0.025-0.390-0.110-0.306-0.3060.481-0.3860.592-0.013-0.123-0.2110.1360.164
Flower_No.-0.100-0.0940.0710.2041.000NaN0.065NaNNaNNaN0.096-0.245NaN-0.252-0.2520.000-0.0790.000-0.134-0.224NaN-0.139-0.131
Flowers_total0.1090.0590.005-0.195NaN1.0000.1690.3420.4380.4640.1060.1440.7900.1640.1640.1430.0910.1700.1780.1160.8540.1060.100
Fruit0.9280.2650.4740.2910.0650.1691.0000.8220.3130.5340.2820.1750.0410.1510.1510.5270.2280.5200.2740.1340.0000.7890.732
Fruits_marked0.6840.1870.5290.157NaN0.3420.8221.0000.4500.882-0.044-0.1310.506-0.041-0.0410.406-0.2390.5910.3350.0780.0800.7110.712
Fruits_notmarked0.3210.1280.3840.173NaN0.4380.3130.4501.0000.788-0.085-0.1880.241-0.087-0.0870.293-0.2740.4480.1590.0610.4840.3800.355
Fruits_total0.6280.1880.5370.191NaN0.4640.5340.8820.7881.000-0.060-0.1520.455-0.038-0.0380.352-0.2830.5320.3440.1040.3010.6650.658
ID-0.166-0.1250.293-0.0250.0960.1060.282-0.044-0.085-0.0601.0000.0790.1220.0270.0270.3760.1620.313-0.079-0.0780.061-0.155-0.167
Length_mm0.0320.261-0.435-0.390-0.2450.1440.175-0.131-0.188-0.1520.0791.0000.1410.9120.9120.3820.6600.4790.4700.5560.0850.0360.059
Marked0.1470.0610.140-0.110NaN0.7900.0410.5060.2410.4550.1220.1411.0000.1580.1580.1510.0860.0850.1810.1030.4010.1560.164
Multiplied0.0980.318-0.340-0.306-0.2520.1640.151-0.041-0.087-0.0380.0270.9120.1581.0001.0000.3520.3350.3820.5630.8340.1090.1010.127
Multiplied_cm0.0980.318-0.340-0.306-0.2520.1640.151-0.041-0.087-0.0380.0270.9120.1581.0001.0000.3520.3350.3820.5630.8340.1090.1010.127
Population0.3460.3020.9260.4810.0000.1430.5270.4060.2930.3520.3760.3820.1510.3520.3521.0000.3950.9990.1620.2050.1290.2990.265
Ratio_len/wid-0.151-0.001-0.460-0.386-0.0790.0910.228-0.239-0.274-0.2830.1620.6600.0860.3350.3350.3951.0000.5350.046-0.1810.060-0.143-0.139
Species0.4940.1590.9390.5920.0000.1700.5200.5910.4480.5320.3130.4790.0850.3820.3820.9990.5351.0000.0000.1320.2160.4420.391
Tunnel_len0.3890.470-0.001-0.013-0.1340.1780.2740.3350.1590.344-0.0790.4700.1810.5630.5630.1620.0460.0001.0000.5590.0960.3550.393
Width_mm0.1780.344-0.141-0.123-0.2240.1160.1340.0780.0610.104-0.0780.5560.1030.8340.8340.205-0.1810.1320.5591.0000.0880.1630.189
Wilted0.0050.024-0.093-0.211NaN0.8540.0000.0800.4840.3010.0610.0850.4010.1090.1090.1290.0600.2160.0960.0881.000-0.006-0.024
seeds0.8740.2660.3660.136-0.1390.1060.7890.7110.3800.665-0.1550.0360.1560.1010.1010.299-0.1430.4420.3550.163-0.0061.0000.986
weight0.9250.2780.3480.164-0.1310.1000.7320.7120.3550.658-0.1670.0590.1640.1270.1270.265-0.1390.3910.3930.189-0.0240.9861.000

Missing values

2025-05-17T11:33:27.331580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-17T11:33:27.751661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-17T11:33:28.191645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

EntryEntry_speciesSpeciesPopulationDateIDFlower_No.Length_mmWidth_mmRatio_len/widMultipliedMultiplied_cmTunnel_lenEntr_lenEntr_HeightPhoto_IDCommentsFlowers_totalMarkedWiltedFruits_markedFruits_notmarkedFruits_totalFruitseedsweightAvg.weight
0123I.atropurpureaNET2023-02-263163.455.01.1527273487.0034.8700NaNNaNNaN92803+928201C, F eaten3.01.02.00.00.00.00.00.00.00.000000
1224I.atropurpureaNET2023-02-2610196.078.51.2229307536.0075.360031.611.17.493612NaN3.02.01.01.00.01.01.014.01008.572.035714
2336I.atropurpureaNET2023-03-0510277.163.41.2160884888.1448.881426.714.08.893157Numbers in picsNaNNaNNaNNaNNaNNaN0.00.00.00.000000
3446I.atropurpureaNET2023-03-0529161.057.21.0664343489.2034.8920NaNNaNNaN1231395C2.01.01.00.00.00.00.00.00.00.000000
4535I.atropurpureaNET2023-02-2634162.559.71.0469013731.2537.312531.012.56.6134903NaN2.01.01.00.00.00.00.00.00.00.000000
5644I.atropurpureaNET2023-03-0547157.553.41.0767793070.5030.7050NaNNaNNaN113524NaN6.01.05.00.01.01.00.00.00.00.000000
6713I.atropurpureaNET2023-02-2149174.665.71.1354644901.2249.012226.017.37.2104813C2.01.01.00.00.00.00.00.00.00.000000
789I.atropurpureaNET2023-02-2151189.775.01.1960006727.5067.275032.817.211.9103717NaN18.05.013.00.00.00.00.00.00.00.000000
8910I.atropurpureaNET2023-02-2151288.473.21.2076506470.8864.708831.517.89.9103751NaNNaNNaNNaNNaNNaNNaN0.00.00.00.000000
91011I.atropurpureaNET2023-02-2151376.677.80.9845765959.4859.594831.518.01.1- CutNaNNaNNaNNaNNaNNaNNaN0.00.00.00.000000
EntryEntry_speciesSpeciesPopulationDateIDFlower_No.Length_mmWidth_mmRatio_len/widMultipliedMultiplied_cmTunnel_lenEntr_lenEntr_HeightPhoto_IDCommentsFlowers_totalMarkedWiltedFruits_markedFruits_notmarkedFruits_totalFruitseedsweightAvg.weight
454455189I.petranaYER2023-03-19914161.768.70.8981084238.7942.387924.812.311.1140121NaN4.02.02.02.02.04.01.050.01257.725.154000
455456190I.petranaYER2023-03-19914265.163.41.0268144127.3441.273426.914.28.7140128NaNNaNNaNNaNNaNNaNNaN1.016.0338.721.168750
456457215I.petranaYER2023-03-22917154.063.10.8557843407.4034.074027.511.28.8110827NaN4.01.03.00.03.03.00.00.00.00.000000
45745851I.petranaYER2023-03-16918156.957.40.9912893266.0632.660631.715.69.2104814NaN7.05.02.04.02.06.01.028.0989.235.328571
45845952I.petranaYER2023-03-16918262.854.91.1438983447.7234.477230.312.49.6104831NaNNaNNaNNaNNaNNaNNaN1.041.01157.628.234146
45946053I.petranaYER2023-03-16918361.860.61.0198023745.0837.450833.412.68104858NaNNaNNaNNaNNaNNaNNaN1.050.01482.429.648000
46046154I.petranaYER2023-03-16918456.063.20.8860763539.2035.392034.416.07.7104910NaNNaNNaNNaNNaNNaNNaN1.035.01251.635.760000
461462216I.petranaYER2023-03-22918550.050.40.9920632520.0025.200026.412.39.2111249NaNNaNNaNNaNNaNNaNNaN0.00.00.00.000000
462463279I.petranaYER2023-03-28920153.553.51.0000002862.2528.622524.313.411.5102606NaN1.01.00.01.00.01.01.06.0201.633.600000
463464106I.petranaYER2023-03-16921157.853.31.0844283080.7430.807427.113.09.940614NaN1.01.00.01.00.01.01.060.0586.59.775000